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Large Language Models, Quiz 2

May 2025 term, 3 Aug 2025, Set QIA2

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Question 1

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What is a primary motivation for using subword tokenization (like BPE or WordPiece) instead of word-level tokenization in transformers?

Select one option

A.
It increases vocabulary size exponentially
B.
It completely eliminates the need for a tokenizer
C.
It helps handle out-of-vocabulary words more effectively
D.
It prevents the model from needing positional encoding

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What is a primary motivation for using subword tokenization (like BPE or WordPiece) instead of word-level tokenization in transformers? What is a key implication of scaling laws in large language models? Consider the Following Assertion and Reason pair :\ **Assertion :** In Byte Pair Encoding (BPE), each merge operation adds exactly one new token to the vocabulary.\ **Reason :** BPE helps reduce the size of the initial vocabulary by merging the most frequent tokens.